{"as_of":"2026-08-08T06:44:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:bfbe4ffd234e49e2ee92f45775b8d0089eba5c92f7a2cee2b1dd6d205d13af6b","coverage":[{"denominator":30,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":30,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T18:47:48.077033Z","state":"measured"},{"denominator":30,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":30,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2512.03594/citation-record","integrity":"/paper/2512.03594/integrity","json":"/paper/2512.03594/citation-record.json","paper":"/paper/2512.03594"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T18:47:45.781743Z","title":"An algorithm for path connections and its applications,","venue":null,"work_id":null,"year":1961},"citing_paper":{"arxiv_id":"2512.03594","last_updated":"2026-07-17T02:31:19Z","snapshot_observed_at":"2026-08-06T19:17:45.672534Z","submitted_at":"2025-12-03T09:25:39Z","title":"Accelerating Detailed Routing Convergence through Offline Reinforcement Learning","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-03T18:47:45.781743Z"},"links":{"citing_paper":"/paper/2512.03594"},"observation_digest":"sha256:2163d25eeaef664d5fa6d0fe4ef52f2f53d23701395c5fb4b4dd7be81d12642b","observation_id":"7db5f7c9-4f6f-45e0-8b2b-0752742dd582","resolution":{"observed_at":"2026-08-03T18:47:45.781743Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T18:47:45.845106Z","title":"An interactive maze router with hints,","venue":null,"work_id":null,"year":1988},"citing_paper":{"arxiv_id":"2512.03594","last_updated":"2026-07-17T02:31:19Z","snapshot_observed_at":"2026-08-06T19:17:45.672534Z","submitted_at":"2025-12-03T09:25:39Z","title":"Accelerating Detailed Routing Convergence through Offline Reinforcement Learning","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-03T18:47:45.845106Z"},"links":{"citing_paper":"/paper/2512.03594"},"observation_digest":"sha256:badf48dedd4135d3b5d4253d53e1fc981c779480d5926e1e7377eb152f6b9556","observation_id":"b78ba77f-5808-4e5e-9377-f70611c43ec9","resolution":{"observed_at":"2026-08-03T18:47:45.845106Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T18:47:45.948760Z","title":"A solution to line-routing problems on the continuous plane,","venue":null,"work_id":null,"year":1969},"citing_paper":{"arxiv_id":"2512.03594","last_updated":"2026-07-17T02:31:19Z","snapshot_observed_at":"2026-08-06T19:17:45.672534Z","submitted_at":"2025-12-03T09:25:39Z","title":"Accelerating Detailed Routing Convergence through Offline Reinforcement Learning","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-03T18:47:45.948760Z"},"links":{"citing_paper":"/paper/2512.03594"},"observation_digest":"sha256:01625e959300bab1d2b864928b400adb5686ab9608e3b5050c4126c5f31c7292","observation_id":"5a92f7fb-11ec-4ce9-b0ea-950fca11a1ae","resolution":{"observed_at":"2026-08-03T18:47:45.948760Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T18:47:46.012194Z","title":"Detailed routing by sparse grid graph and minimum-area-captured path search,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2512.03594","last_updated":"2026-07-17T02:31:19Z","snapshot_observed_at":"2026-08-06T19:17:45.672534Z","submitted_at":"2025-12-03T09:25:39Z","title":"Accelerating Detailed Routing Convergence through Offline Reinforcement Learning","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-03T18:47:46.012194Z"},"links":{"citing_paper":"/paper/2512.03594"},"observation_digest":"sha256:cb7c2c7013f0e05af75af6cad5fcb430e3a8beb68dae18265558d180b39828e6","observation_id":"8b367004-2335-4471-b7e5-d9b903e3114f","resolution":{"observed_at":"2026-08-03T18:47:46.012194Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T18:47:46.110477Z","title":"OpenROAD: Toward a Self-Driving, Open-Source Digital Layout Implementation Tool Chain,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2512.03594","last_updated":"2026-07-17T02:31:19Z","snapshot_observed_at":"2026-08-06T19:17:45.672534Z","submitted_at":"2025-12-03T09:25:39Z","title":"Accelerating Detailed Routing Convergence through Offline Reinforcement Learning","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-03T18:47:46.110477Z"},"links":{"citing_paper":"/paper/2512.03594"},"observation_digest":"sha256:4900c511847a0776645a81c53a1a96c79a179476ff2f93e8676e43d4537ae2fc","observation_id":"c192c183-de62-4502-aed3-fc4541fa2273","resolution":{"observed_at":"2026-08-03T18:47:46.110477Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T18:47:46.190054Z","title":"INVITED: Toward an Open- Source Digital Flow: First Learnings from the OpenROAD Project,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2512.03594","last_updated":"2026-07-17T02:31:19Z","snapshot_observed_at":"2026-08-06T19:17:45.672534Z","submitted_at":"2025-12-03T09:25:39Z","title":"Accelerating Detailed Routing Convergence through Offline Reinforcement Learning","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-03T18:47:46.190054Z"},"links":{"citing_paper":"/paper/2512.03594"},"observation_digest":"sha256:eb6e2741f461ba51c8ecabe252f7153eebb9c6045ecca5abe202fe9355b21f7e","observation_id":"b815462c-3165-467d-a899-9039d7e6d3e9","resolution":{"observed_at":"2026-08-03T18:47:46.190054Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T18:47:46.337214Z","title":"Provably efficient offline reinforcement learning with perturbed data sources,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2512.03594","last_updated":"2026-07-17T02:31:19Z","snapshot_observed_at":"2026-08-06T19:17:45.672534Z","submitted_at":"2025-12-03T09:25:39Z","title":"Accelerating Detailed Routing Convergence through Offline Reinforcement Learning","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-03T18:47:46.337214Z"},"links":{"citing_paper":"/paper/2512.03594"},"observation_digest":"sha256:7bf70b7c78cc1f57a1d8613956b56e23a23123fd729ab1fa86aa20b0d4d616a7","observation_id":"d34a413b-7882-45b5-b811-1c5ece2f53d2","resolution":{"observed_at":"2026-08-03T18:47:46.337214Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T18:47:46.530229Z","title":"Robust deep reinforcement learning with adaptive adversarial perturbations in action space,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.03594","last_updated":"2026-07-17T02:31:19Z","snapshot_observed_at":"2026-08-06T19:17:45.672534Z","submitted_at":"2025-12-03T09:25:39Z","title":"Accelerating Detailed Routing Convergence through Offline Reinforcement Learning","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-03T18:47:46.530229Z"},"links":{"citing_paper":"/paper/2512.03594"},"observation_digest":"sha256:44641f2d347ad6c82d364dc6c1b62287de25899c79690805f18736e934117ed2","observation_id":"afe93ef0-4add-4915-ba80-cff9a034aef1","resolution":{"observed_at":"2026-08-03T18:47:46.530229Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T18:47:46.612192Z","title":"Conservative q-learning for offline reinforcement learning,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2512.03594","last_updated":"2026-07-17T02:31:19Z","snapshot_observed_at":"2026-08-06T19:17:45.672534Z","submitted_at":"2025-12-03T09:25:39Z","title":"Accelerating Detailed Routing Convergence through Offline Reinforcement Learning","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-03T18:47:46.612192Z"},"links":{"citing_paper":"/paper/2512.03594"},"observation_digest":"sha256:9a3fe72c746b1045efe44ca8efe21b019483fddb986ce7f7552b53f31d8922e6","observation_id":"dd7172f0-d279-4ca8-a304-6649e894d0d3","resolution":{"observed_at":"2026-08-03T18:47:46.612192Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T18:47:46.667308Z","title":"Bidirectional heuristic search reconsidered,","venue":null,"work_id":null,"year":1997},"citing_paper":{"arxiv_id":"2512.03594","last_updated":"2026-07-17T02:31:19Z","snapshot_observed_at":"2026-08-06T19:17:45.672534Z","submitted_at":"2025-12-03T09:25:39Z","title":"Accelerating Detailed Routing Convergence through Offline Reinforcement Learning","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-03T18:47:46.667308Z"},"links":{"citing_paper":"/paper/2512.03594"},"observation_digest":"sha256:c8be15d1c5ac3130b5289234fc7266f2610692beaf55b3c6a3040df4e9d3fa73","observation_id":"aa7b796e-d1ec-4e10-bf50-72d298138cab","resolution":{"observed_at":"2026-08-03T18:47:46.667308Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T18:47:46.724494Z","title":"Evaluation of beol design rule impacts using an optimal ilp-based detailed router,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2512.03594","last_updated":"2026-07-17T02:31:19Z","snapshot_observed_at":"2026-08-06T19:17:45.672534Z","submitted_at":"2025-12-03T09:25:39Z","title":"Accelerating Detailed Routing Convergence through Offline Reinforcement Learning","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-03T18:47:46.724494Z"},"links":{"citing_paper":"/paper/2512.03594"},"observation_digest":"sha256:7140bc21e846e81dfab1ebfba63404a4d863e08404c5574982dc795a332fa5fb","observation_id":"e14c4f79-f0b3-42c5-816d-304c6c210c0f","resolution":{"observed_at":"2026-08-03T18:47:46.724494Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T18:47:46.785066Z","title":"Tritonroute: The open-source detailed router,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2512.03594","last_updated":"2026-07-17T02:31:19Z","snapshot_observed_at":"2026-08-06T19:17:45.672534Z","submitted_at":"2025-12-03T09:25:39Z","title":"Accelerating Detailed Routing Convergence through Offline Reinforcement Learning","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-03T18:47:46.785066Z"},"links":{"citing_paper":"/paper/2512.03594"},"observation_digest":"sha256:7c02e6e1b83d764155351af58d7c942500d3129817379c50f762c10c806dbd55","observation_id":"170ab79d-c0d6-48fa-8b59-cb9e8d07a05b","resolution":{"observed_at":"2026-08-03T18:47:46.785066Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T18:47:46.865988Z","title":"Gridless pin access in detailed routing,","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2512.03594","last_updated":"2026-07-17T02:31:19Z","snapshot_observed_at":"2026-08-06T19:17:45.672534Z","submitted_at":"2025-12-03T09:25:39Z","title":"Accelerating Detailed Routing Convergence through Offline Reinforcement Learning","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-03T18:47:46.865988Z"},"links":{"citing_paper":"/paper/2512.03594"},"observation_digest":"sha256:4a45225722da495fadccefb3b6f8f623507da6e02af2ef6da5057616442713eb","observation_id":"3db3e43e-a651-480d-8195-a10894921d25","resolution":{"observed_at":"2026-08-03T18:47:46.865988Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T18:47:46.919307Z","title":"Self-aligned double patterning lithography aware detailed routing with color preassignment,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2512.03594","last_updated":"2026-07-17T02:31:19Z","snapshot_observed_at":"2026-08-06T19:17:45.672534Z","submitted_at":"2025-12-03T09:25:39Z","title":"Accelerating Detailed Routing Convergence through Offline Reinforcement Learning","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-03T18:47:46.919307Z"},"links":{"citing_paper":"/paper/2512.03594"},"observation_digest":"sha256:8be6330f1bd7266c0158e3c1f6ee5401ff5d6df19078b46c1fd72168cc04b47b","observation_id":"6e0527da-3755-4e9b-a554-c091362f8351","resolution":{"observed_at":"2026-08-03T18:47:46.919307Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T18:47:47.003661Z","title":"Overlay-aware detailed routing for self-aligned double patterning lithography using the cut process,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2512.03594","last_updated":"2026-07-17T02:31:19Z","snapshot_observed_at":"2026-08-06T19:17:45.672534Z","submitted_at":"2025-12-03T09:25:39Z","title":"Accelerating Detailed Routing Convergence through Offline Reinforcement Learning","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-03T18:47:47.003661Z"},"links":{"citing_paper":"/paper/2512.03594"},"observation_digest":"sha256:2b2b72eb07bc631307a68c6a1e9a2e619ccbb4dc62c03cb5f8ada4e690bbbc3f","observation_id":"b0b565a9-c759-46cf-a67a-6f389278fa97","resolution":{"observed_at":"2026-08-03T18:47:47.003661Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T18:47:47.116162Z","title":"Detailed routing algorithms for advanced technology nodes,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2512.03594","last_updated":"2026-07-17T02:31:19Z","snapshot_observed_at":"2026-08-06T19:17:45.672534Z","submitted_at":"2025-12-03T09:25:39Z","title":"Accelerating Detailed Routing Convergence through Offline Reinforcement Learning","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-03T18:47:47.116162Z"},"links":{"citing_paper":"/paper/2512.03594"},"observation_digest":"sha256:f95882fe99f4eca39905e3fc3082649ac4495ed3124d7d88b237e217f645a2d7","observation_id":"199af8c5-d087-497f-9118-2df85842b170","resolution":{"observed_at":"2026-08-03T18:47:47.116162Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T18:47:47.202699Z","title":"Ispd 2018 initial detailed routing contest and benchmarks,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2512.03594","last_updated":"2026-07-17T02:31:19Z","snapshot_observed_at":"2026-08-06T19:17:45.672534Z","submitted_at":"2025-12-03T09:25:39Z","title":"Accelerating Detailed Routing Convergence through Offline Reinforcement Learning","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-03T18:47:47.202699Z"},"links":{"citing_paper":"/paper/2512.03594"},"observation_digest":"sha256:41d30d38c1cb4b93e19a1e5a4491761465373a4c16407aa90be55983a636e6bc","observation_id":"49cc688f-fb25-4a86-b24d-3135442df6c4","resolution":{"observed_at":"2026-08-03T18:47:47.202699Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T18:47:47.284508Z","title":"Ispd 2019 initial detailed routing contest and benchmark with advanced routing rules,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2512.03594","last_updated":"2026-07-17T02:31:19Z","snapshot_observed_at":"2026-08-06T19:17:45.672534Z","submitted_at":"2025-12-03T09:25:39Z","title":"Accelerating Detailed Routing Convergence through Offline Reinforcement Learning","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-03T18:47:47.284508Z"},"links":{"citing_paper":"/paper/2512.03594"},"observation_digest":"sha256:58ea86ca510d159903cce06b48a26802ed21f46f53823bfd8c71aa9fb62c59ca","observation_id":"6e008fbf-2bdc-44c4-a19a-12102d7d1c72","resolution":{"observed_at":"2026-08-03T18:47:47.284508Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T18:47:47.364817Z","title":"Tritonroute: An initial detailed router for advanced vlsi technologies,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2512.03594","last_updated":"2026-07-17T02:31:19Z","snapshot_observed_at":"2026-08-06T19:17:45.672534Z","submitted_at":"2025-12-03T09:25:39Z","title":"Accelerating Detailed Routing Convergence through Offline Reinforcement Learning","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-03T18:47:47.364817Z"},"links":{"citing_paper":"/paper/2512.03594"},"observation_digest":"sha256:da21e3cd520c487c4be449ad38c3020840504fd124d1a981949f218716ede8e5","observation_id":"b795702a-733f-46ae-8a4b-401345b822e9","resolution":{"observed_at":"2026-08-03T18:47:47.364817Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T18:47:47.419359Z","title":"Explainable drc hotspot prediction with random forest and shap tree explainer,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2512.03594","last_updated":"2026-07-17T02:31:19Z","snapshot_observed_at":"2026-08-06T19:17:45.672534Z","submitted_at":"2025-12-03T09:25:39Z","title":"Accelerating Detailed Routing Convergence through Offline Reinforcement Learning","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-03T18:47:47.419359Z"},"links":{"citing_paper":"/paper/2512.03594"},"observation_digest":"sha256:05beb0d66006615c0df84450f72b7c3c155136598e1872de322102a309cf5e5c","observation_id":"b46cc373-4175-4fc9-87d6-46d3cd5ae497","resolution":{"observed_at":"2026-08-03T18:47:47.419359Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T18:47:47.475131Z","title":"Pin accessibility and routing congestion aware drc hotspot prediction for designs in advanced technology nodes with consolidated practical applicability and sustainability,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.03594","last_updated":"2026-07-17T02:31:19Z","snapshot_observed_at":"2026-08-06T19:17:45.672534Z","submitted_at":"2025-12-03T09:25:39Z","title":"Accelerating Detailed Routing Convergence through Offline Reinforcement Learning","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-03T18:47:47.475131Z"},"links":{"citing_paper":"/paper/2512.03594"},"observation_digest":"sha256:f9e66f3d9e5d739051df2c63cf116d4267c396960725bbb952a7b4c6a4ef30c2","observation_id":"160b0f0d-f532-461c-ad7d-b31fe1018348","resolution":{"observed_at":"2026-08-03T18:47:47.475131Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T18:47:47.554066Z","title":"Drc hotspot prediction at sub-10nm process nodes using customized convolutional network,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2512.03594","last_updated":"2026-07-17T02:31:19Z","snapshot_observed_at":"2026-08-06T19:17:45.672534Z","submitted_at":"2025-12-03T09:25:39Z","title":"Accelerating Detailed Routing Convergence through Offline Reinforcement Learning","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-03T18:47:47.554066Z"},"links":{"citing_paper":"/paper/2512.03594"},"observation_digest":"sha256:6773c1d5bd607448ae4432f43ac65adc1d6bc3ecb059fe177a8950f404e423d4","observation_id":"741fdceb-371b-44d0-8cc7-a391280f1089","resolution":{"observed_at":"2026-08-03T18:47:47.554066Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T18:47:47.609230Z","title":"Reinforcement learning guided detailed routing for custom circuits,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2512.03594","last_updated":"2026-07-17T02:31:19Z","snapshot_observed_at":"2026-08-06T19:17:45.672534Z","submitted_at":"2025-12-03T09:25:39Z","title":"Accelerating Detailed Routing Convergence through Offline Reinforcement Learning","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-03T18:47:47.609230Z"},"links":{"citing_paper":"/paper/2512.03594"},"observation_digest":"sha256:5dcbcb9c4f6dc7bcfc6177bfb9479786d8482026441e9072604557b8a1eed27a","observation_id":"49b3d2ac-5f67-44b9-aaed-908285f48797","resolution":{"observed_at":"2026-08-03T18:47:47.609230Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T18:47:47.690827Z","title":"Enhancing accuracy of deep learning algorithms by training with low-discrepancy sequences,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2512.03594","last_updated":"2026-07-17T02:31:19Z","snapshot_observed_at":"2026-08-06T19:17:45.672534Z","submitted_at":"2025-12-03T09:25:39Z","title":"Accelerating Detailed Routing Convergence through Offline Reinforcement Learning","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-03T18:47:47.690827Z"},"links":{"citing_paper":"/paper/2512.03594"},"observation_digest":"sha256:978eda3c5249fcd1e5a3eeaf68cd93f8a1100388cf84cf2520896f6d2701954b","observation_id":"e2154852-0522-4858-b916-8496c0986b87","resolution":{"observed_at":"2026-08-03T18:47:47.690827Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T18:47:47.775448Z","title":"Sampling based on sobol′ sequences for monte carlo techniques applied to building simulations,","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2512.03594","last_updated":"2026-07-17T02:31:19Z","snapshot_observed_at":"2026-08-06T19:17:45.672534Z","submitted_at":"2025-12-03T09:25:39Z","title":"Accelerating Detailed Routing Convergence through Offline Reinforcement Learning","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-03T18:47:47.775448Z"},"links":{"citing_paper":"/paper/2512.03594"},"observation_digest":"sha256:558de07762602501ebc31a56276fe3e900b9e64fc4f1c17ce05bcc94828855c3","observation_id":"c7968de6-e901-4d85-ba4b-6f659fac5a74","resolution":{"observed_at":"2026-08-03T18:47:47.775448Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T18:47:47.831705Z","title":"Bridging Academic Open-Source EDA to Real-World Usability,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2512.03594","last_updated":"2026-07-17T02:31:19Z","snapshot_observed_at":"2026-08-06T19:17:45.672534Z","submitted_at":"2025-12-03T09:25:39Z","title":"Accelerating Detailed Routing Convergence through Offline Reinforcement Learning","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-03T18:47:47.831705Z"},"links":{"citing_paper":"/paper/2512.03594"},"observation_digest":"sha256:ad40f680fc98efb7685e03f7d6ca55af87266559a9b628b884949bc29b1c988b","observation_id":"3ae3656e-d78f-42a1-86c4-72f261d169d6","resolution":{"observed_at":"2026-08-03T18:47:47.831705Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T18:47:47.917700Z","title":"Offline reinforcement learning: Tutorial, review, and perspectives on open problems,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2512.03594","last_updated":"2026-07-17T02:31:19Z","snapshot_observed_at":"2026-08-06T19:17:45.672534Z","submitted_at":"2025-12-03T09:25:39Z","title":"Accelerating Detailed Routing Convergence through Offline Reinforcement Learning","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-03T18:47:47.917700Z"},"links":{"citing_paper":"/paper/2512.03594"},"observation_digest":"sha256:fd1549d9d53e31302819d58211dcdb4fdfbb7a51cafc0db1ba5a6eeb4d301574","observation_id":"50c48864-945b-4333-aa09-896e4faa0adf","resolution":{"observed_at":"2026-08-03T18:47:47.917700Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T18:47:47.972632Z","title":"Reinforcement learning: An introduction,","venue":null,"work_id":null,"year":1998},"citing_paper":{"arxiv_id":"2512.03594","last_updated":"2026-07-17T02:31:19Z","snapshot_observed_at":"2026-08-06T19:17:45.672534Z","submitted_at":"2025-12-03T09:25:39Z","title":"Accelerating Detailed Routing Convergence through Offline Reinforcement Learning","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-03T18:47:47.972632Z"},"links":{"citing_paper":"/paper/2512.03594"},"observation_digest":"sha256:ba184a9fcb3a8c40c677649f884a72a02fee668b4899e0fee352147d66784d74","observation_id":"8a732640-613d-450b-b6f5-5d863cbd75ae","resolution":{"observed_at":"2026-08-03T18:47:47.972632Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2111.03788","last_updated":"2022-12-03T12:03:07Z","snapshot_observed_at":"2026-07-06T12:05:59.258427Z","submitted_at":"2021-11-06T03:09:39Z","title":"d3rlpy: An Offline Deep Reinforcement Learning Library","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.03788","snapshot_observed_at":"2026-08-03T18:47:48.035582Z","title":"d3rlpy: An offline deep reinforcement learning library,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2512.03594","last_updated":"2026-07-17T02:31:19Z","snapshot_observed_at":"2026-08-06T19:17:45.672534Z","submitted_at":"2025-12-03T09:25:39Z","title":"Accelerating Detailed Routing Convergence through Offline Reinforcement Learning","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-03T18:47:48.035582Z"},"links":{"cited_paper":"/paper/2111.03788","citing_paper":"/paper/2512.03594"},"observation_digest":"sha256:1a5d9c11b9ba4fa754409892bfbba16bf9351eeec00d08162660d753052eedb2","observation_id":"94639c8c-c7e7-4a31-bc90-9941a0bc8b41","resolution":{"observed_at":"2026-08-03T18:47:48.035582Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.09055","last_updated":"2020-07-17T15:30:38Z","snapshot_observed_at":"2026-08-02T00:32:54.352911Z","submitted_at":"2020-07-17T15:30:38Z","title":"Hyperparameter Selection for Offline Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.09055","snapshot_observed_at":"2026-08-03T18:47:48.077033Z","title":"Hyperparameter selection for offline reinforcement learning,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2512.03594","last_updated":"2026-07-17T02:31:19Z","snapshot_observed_at":"2026-08-06T19:17:45.672534Z","submitted_at":"2025-12-03T09:25:39Z","title":"Accelerating Detailed Routing Convergence through Offline Reinforcement Learning","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-03T18:47:48.077033Z"},"links":{"cited_paper":"/paper/2007.09055","citing_paper":"/paper/2512.03594"},"observation_digest":"sha256:81b203b4f1c785cf07d915a5b10807774b1451c1df9d1e648c1dd71e83f4e30f","observation_id":"ff3857be-c107-487d-924d-b1acc5806e36","resolution":{"observed_at":"2026-08-03T18:47:48.077033Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2512.03594","last_updated":"2026-07-17T02:31:19Z","latest_version":2,"primary_category":"cs.AR","snapshot_observed_at":"2026-08-06T19:17:45.672534Z","submitted_at":"2025-12-03T09:25:39Z","title":"Accelerating Detailed Routing Convergence through Offline Reinforcement Learning"},"reference_resolution":{"displayed":30,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":30,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":30},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2512.03594."}